Effective implementation of low probability of intercept (LPI) techniques is crucial for enhancing the survivability of radar systems in electronic warfare scenarios. This article explores the use of reinforcement learning in dynamically generating LPI signals in unknown adversarial environments encountered by electronic intelligence (ELINT) systems. We develop a Markov decision model to guide the LPI radar in mitigating power-based interceptions by jointly optimizing transmit signal power and modulation strategies. The interception efficacy of ELINT and the radar's normalized instantaneous transmit power are integrated to comprehensively evaluate LPI performance and radar detection capabilities. A tunable weighting factor facilitates adaptable adjustments between these objectives during decision making. We validate the efficacy of the proposed approach by numerical simulations. Assessment of LPI efficacy is conducted through the analysis of time–frequency modulation signals observed by ELINT, while detection performance is evaluated through synthetic aperture radar imaging tasks.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reinforcement-Learning-Enhanced Adaption of Signal Power and Modulation for LPI Radar System


    Contributors:
    Yuan, Ye (author) / Liu, Xinyu (author) / Zhang, Tianxian (author) / Cui, Guolong (author) / Kong, Lingjiang (author)


    Publication date :

    2024-12-01


    Size :

    4204597 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Reinforcement Learning-based Joint Frame Length and Rate Adaption for WLAN Network

    Zhou, Lihong / Fang, Xuming / He, Rong et al. | IEEE | 2023


    Intake adaption system

    BANKS III GALE C | European Patent Office | 2021

    Free access


    Pollution Practical Research of Adaption Cruise Control System Radar During Winter Usage Condition

    Ivanov, A. M. / Andreev, A. N. / Dyakov, Ph. K. et al. | IEEE | 2022


    METHOD FOR RESTRAINT DEPLOYMENT ADAPTION AND SYSTEM FOR RESTRAINT DEPLOYMENT ADAPTION

    VERMEULIN FRANCOIS-XAVIER / ROCCHISANI CLAIRE / DEVAVRY ARNAUD et al. | European Patent Office | 2024

    Free access